1. Field of the Invention
The present invention relates to a method of building a defect database, and more particularly, to a method of building a defect database applied to an automatic defect classification system.
2. Description of the Prior Art
In the semiconductor fabricating process, some small particles and defects are unavoidable. As the size of devices shrinks and the integration of circuits increases gradually, those small particles or defects affect the property of the integrated circuits more seriously. For improving the reliability of semiconductor devices, a plurality of defect detections are performed continuously, and the detected defects are further examined for analyzing a root cause of the defects. According to the result of the defect root cause analysis, process parameters are tuned correspondingly to reduce a presence of defects or particles so as to improve the yield and reliability of the semiconductor fabricating process.
Please refer to FIG. 1, which is a schematic diagram of a prior art method of a defect detection and analysis process. As shown in FIG. 1, a sampling 12 is first performed to obtain a test sample for inline product wafers. Then, a defect inspection 14 is performed according to the test sample. In the defect inspection 14, a large scale of scan is typically performed to detect defects roughly. After that, a defect review 18 is performed to examine the detected defects in detail by some proper machines such as an SEM and a defect root cause analysis 22 is performed according to a result of the defect review 18. Since the defect review 18 is a heavy loading and time-consuming work, it is impossible to perform a defect review 18 for all detected defects. Typically, a sampling for selecting some defects is required to perform the defect review 18. Normally, a defect classification 16 is performed after finishing the detect inspection 14, to separate the defects into different defect types. Then, a certain amount of defects of each defect type are picked up for the defect review 18 and the following defect analysis 22, to find the root cause of the defects. Therefore, the defect generation can be reduced by tuning process parameters properly.
In the prior art technology, most of the defect classification 16 is manual work. Thus, much effort and time must be spent. However, nowadays a technology of automatic defect classification (ADC) is developed and the work of defect classification is performed by machines instead of human beings. For example, some defect inspection machines comprise an additional function of automatic defect classification. When the defect inspection 14 is done, the defect classification 16 is then performed to provide a report of defect inspection and grouping to the inline engineers for performing the following defect review 18 and defect analysis 22.
Normally, those defect classification machines are connected to a defect database which comprises a defect classification recipe stored therein to control the defect classification work. In other words, the accuracy of the defect classification is dependent on the classification recipe. However, since there are many differences between the defects caused by different processes, the methods of definition or classification of each defect type are also varied correspondingly. Thus, before an automatic defect classification is performed for the inline product, a defect database with proper defect classification recipe must be built so as to make the automatic defect classification machines work properly.
Please refer to FIG. 2, which is a schematic diagram of a conventional method of building a defect database in the prior art. As shown in FIG. 2, a sampling 32 is first performed to obtain a sample wafer. Then, a defect inspection, defect classification, and defect review are performed to collect defect information 34. After certain amounts of defect information of each defect type are collected, a database can be built according to this defect information. This defect information is used to train the defect classification machines and set a defect classification recipe 36. After that, an automatic defect classification can be performed according to the defect classification recipe. It is sure that at least one verifying step 38 is performed. According to a result of manual defect classification, the accuracy of the automatic defect classification can be judged or corrected before being put online 40.
As mentioned above, collecting the defect information 34 is needed before building a defect database in the conventional method. For improving the accuracy of the automatic defect classification, a large amount of defect samples must be reviewed by the SEM for collecting defect information. Normally, 30 to 50 defect samples are required for each defect type to build a defect database. Thus, taking a fabricating process generating 6 defect types as an example, 200 to 300 defect samples must be reviewed manually. It typically takes two months for building a defect database for a fabricating process. In addition, since defects of different defect types may have different probabilities of appearance, sometimes a defect type may have an extremely low probability of appearance. In that cases, a lot of test samples must be used for setting the defect classification recipe, leading to an increase of the handicap of building a defect database.
Furthermore, due to the progression of the semiconductor technology and some economic consideration, the size of wafers increases from 8 inches to 12 inches and the line width reduces from 0.18 μm to 0.13 μm and even below 0.1 μm. In the process from testing into mass production, it is obvious that the fabricating processes have to be changed or tuned many times in a short time. However, the conventional method of building a defect database always requires lots of time, leading to a delay of the defect root cause analysis. Thus, a quick and accurate method of building a defect database is strongly required to solve the aforementioned problems.